Analytical Procedure for Timber−Concrete Composite (TCC) System with Mechanical Connectors
Bibliographic record
Abstract
In the construction of modern multi-storey mass timber structures, a composite floor system commonly specified by structural engineers is the timber–concrete composite (TCC) system, where a mass timber beam or mass timber panel (MTP) is connected to a concrete slab with mechanical connectors. The design of TCC floor systems has not been addressed in timber design standards due to a lack of suitable analytical models for predicting the serviceability and safety performance of these systems. Moreover, the interlayer connection properties have a large influence on the structural performance of a TCC system. These connection properties are often generated by testing. In this paper, an analytical approach for designing a TCC floor system is proposed that incorporates connection models to predict connection properties from basic connection component properties such as embedment and withdrawal strength/stiffness of the connector, thereby circumventing the need to perform connection tests. The analytical approach leads to the calculation of effective bending stiffness, forces in the connectors, and extreme stresses in concrete and timber of the TCC system, and can be used in design to evaluate allowable floor spans under specific design loads and criteria. An extensive parametric analysis was also conducted following the analytical procedure to investigate the TCC connection and system behaviour. It was observed that the screw spacing and timber thickness remain the most important parameters which significantly influence the TCC system behaviour.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".